In recent years, the demands for causing robots to perform works have grown, such as assembly works in factories. A robot handling an object without a fixed position, posture or type, for example, may require a device for measuring the position, posture or type, for example, of the object. A visual sensor may be widely used as the device for measuring the position, posture or type, for example, of the object.
Causing a robot to perform a work such as an advanced assembly work may require discrimination of an object with a visual sensor, for example. Hitherto, studies have been carried out on identification of the type and position/posture of a part by comparing form information such as CAD data of an object and two-dimensional or three-dimensional information acquired with a visual sensor, for example. Alternatively, an identification method has been studied actively which identifies the type of an object appearing on an input image by causing a computer to learn a feature value extracted from an image of an object acquired with an image-pickup unit.
Generally, identification based on passive vision has been used for discriminating an object. The identification based on passive vision may identify an object by using an input image acquired with a fixed camera. This configuration is difficult to discriminate objects having similar appearances. When it is difficult to acquire sufficient information from a single image, the camera may be actively controlled so as to image an object from a plurality of viewpoints to acquire information. One method in the past selects a behavior such that the mutual entropy between an input image and an image acquired after the behavior may be the highest (NPL 1). This may be available as an example of the method which actively controls a camera so as to image an object from a plurality of viewpoints and acquire information.
According to the method described in NPL 1, mutual entropies for all imaging conditions are acquired by simulating the results online. This may increase the amount of calculations and take a long time for the calculation for setting a new imaging condition. The high number of possible patterns of internal states during the calculation makes advance calculation and caching difficult. Thus, the method described in NPL 1 is difficult to quickly set an imaging condition.